Linear regression estimates the relationship between independent and dependent variables, while logistic regression estimates the probability of an outcome

Study Notes

Types of Regression

Simple linear regression is used to identify the relationship between a continuous dependent variable and one independent variable.
Multiple linear regression is used to identify the relationship between a continuous dependent variable and one or more independent variables.

Linear Regression

The method used to calculate the line of best fit in linear regression is the Ordinary Least Squares (OLS) method.
The OLS method finds the best-fitting line that minimizes the sum of the squared errors.

Linear vs. Logistic Regression

Linear regression is used to predict a continuous outcome variable, whereas logistic regression is used to predict a binary outcome variable.
Linear regression assumes a linear relationship between the independent and dependent variables, whereas logistic regression assumes a non-linear relationship.

Test your knowledge of linear and logistic regression models in this quiz! Learn about the differences between the two models, their uses, and how to compute them using Python and R. Identify the relationship between dependent and independent variables and gain a better understanding of these popular data science models.